Research Topic

[Hyper-QC] Topic: MEMS Reliability

Team: Min Shi, Steve Whalen, Ellad Tadmor

Collaboration: Jia-Liang Le (U. Minnesota), Roberto Ballarini (U. Houston)

Funding: National Science Foundation (CMMI/MOM program)

Figure: QC simulations of systems, like the double edge notched (DEN) specimen in the figure, will be used to evaluate the characteristics of the damage zone in polycrystalline silicon samples.

Description: Micro-electro-mechanical Systems (MEMS) devices typically need to be designed against a very low failure probability, which is on the order of 10-4 or lower. Experimental determination of the target strength for such a low failure probability requires the testing of tens of thousands of specimens, which can be cost prohibitive for the design process. Therefore, a fundamental understanding of the probabilistic failure of MEMS devices is of paramount importance for design. Currently available probabilistic models for predicting the mechanical failure of MEMS structures are based on classical Weibull statistics. Significant advances in experimental techniques for measuring the strength of MEMS devices have produced data that have unambiguously demonstrated that the strength distributions consistently deviate from the Weibull statistics. In addition, it has been shown that the Weibull statistics cannot be used to extrapolate the failure statistics of MEMS structures across different sizes and geometries. The lack of a mechanics-based predictive probabilistic model for brittle MEMS materials and structures motivates this project.

The goal of the research is to develop a novel multi-scale probabilistic model for the failure of brittle MEMS structures under static loading. The model will be derived from a nonlocal finite weakest link model, for which the statistical parameters will be evaluated by fine-scale stochastic quasicontinuum simulations that capture the probabilistic failure of the damage zone. The proposed model will be verified by a series of carefully conducted high-throughput tension experiments of poly-Si samples that will produce strength histograms for different size and shape specimens.